This disclosure relates to predicting viscosity in heavy oil formations and, more particularly, to predicting viscosity in heavy oil formations that include water such as clay-bound water.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as an admission of any kind.
Heavy oil constitutes a large quantity of the total oil resources of the world. The abundance of heavy oil resources, decline in the production of conventional oil reservoirs, and high oil prices have led to a renewed interest in producing heavy oil. The viscosity of heavy oils may vary substantially. The American Petroleum Institute defines gravities of heavy oils to cover a wide range, from 22 for light heavy oils to 10 for extraheavy oils. Accurately characterizing heavy oil properties, such as viscosity, allows operators to determine optimal recovery techniques and to predict production rates and recoverable oil volumes.
Some downhole tools that can be used to predict the viscosity of oil collect measurements of nuclear magnetic resonance (NMR) in a wellbore through a geological formation of interest. Downhole NMR tools may measure the response of nuclear spins in formation fluids to applied magnetic fields. The measurements obtained by downhole NMR tools may include distributions of a first relaxation time T1, a second relaxation time T2, or diffusion, or a combination of these. For example, a downhole NMR tool may measure just T2 distribution, or the tool may measure a joint T1-T2 distribution or T1-T2-D distribution.
Empirical correlations have been proposed to quantitatively relate NMR T2 relaxation time of crude oils to viscosity. For example, it has been proposed that the logarithmic mean of the T2 distribution (T2LM) of crude oil shows inverse power-law dependence on viscosity (η), given as:
                              T                      2            ⁢            LM                          =                              1200                          η              0.9                                .                                    (        1        )            Here, T2LM is in milliseconds and η is in centipoise. The correlation was modified to account for dissolved oxygen in crude oils, as shown below:
                              η          =                      9.56            ⁢                          T                              T                                  2                  ,                  LM                                                                    ,                            (        2        )            where T is temperature in Kelvin. Other expressions relating a diffusion coefficient and viscosity also exist in literature.
Yet these techniques do not always provide accurate, dependable answers under certain conditions. Indeed, many heavy oil formations include water (in particular, clay-bound water), but the responses of heavy oil and water may overlap in the T2 domain. As such, predicting viscosity based on T2 responses may be confounded in the presence of water, such as clay-bound water. Therefore, there is a need for a method for accurate estimation of viscosity of heavy oils in the presence of water.